Regional integration as a remedy against technological dependency? The contradictions in the calls for regional integration in Latin American AI strategies
Bibliographic record
Abstract
The rise of regional integration and South-South cooperation as an imperative in international relations and trade has been widely discussed, especially with the strengthening of BRICS+ (Brazil, Russia, India, China, South Africa), and Pan-African movements. The idea of a world with multiple powers giving rise to diverse social and economic imperatives for technoscientific innovation is finding traction among scholars, including researchers in science and technology studies (STS), who hope this new order can challenge entrenched liberal epistemological and infrastructural hegemony, even though skepticism remains about actual challenges to asymmetries in global science and technology circulation. This paper offers an empirical contribution to the issues of South-South cooperation and multipolarity in the context of regional integration by examining artificial intelligence (AI) policy developments in Latin America. Analyzing the national AI strategies of seven Latin American countries, and engaging with contributions from dependency theorists, the research reveals how aspirations for Latin America to lead in AI innovation often replicate technology policy imperatives from the Global North. This contradictory dynamic perpetuates dependent development models and reinforces Latin America’s peripheral role in technological systems. The paper advocates for increased attention in STS research regarding the contradictions of South-South cooperation and multipolarity, particularly in contexts of economic and technological dependency.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.009 |
| Science and technology studies | 0.002 | 0.020 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".